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CVE-2025-46153

Good to know:

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Date: September 24, 2025

PyTorch before 3.7.0 has a bernoulli_p decompose function in decompositions.py even though it lacks full consistency with the eager CPU implementation, negatively affecting nn.Dropout1d, nn.Dropout2d, and nn.Dropout3d for fallback_random=True.

Severity Score

Related Resources (7)

https://github.com/pytorch/pytorch/compare/v2.6.0...v2.7.0
https://github.com/pytorch/pytorch/issues/142853
https://github.com/pytorch/pytorch/pull/143460
https://gist.github.com/shaoyuyoung/4bcefba4004f8271e64b5185c95a248a
https://nvd.nist.gov/vuln/detail/CVE-2025-46153
https://gist.github.com/shaoyuyoung/e636f2e7a306105b7e96809e2b85c28a
https://www.mend.io/vulnerability-database/CVE-2025-46153

Severity Score

Weakness Type (CWE)

Inefficient CPU Computation

CWE-1176

Top Fix

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Upgrade Version

Upgrade to version torch - 2.7.0;pytorch - 2.7.0;https://github.com/pytorch/pytorch.git - v2.7.0

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CVSS v3.1

Base Score:
Attack Vector (AV): NETWORK
Attack Complexity (AC): LOW
Privileges Required (PR): NONE
User Interaction (UI): NONE
Scope (S): UNCHANGED
Confidentiality (C): LOW
Integrity (I): NONE
Availability (A): NONE

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